Industrial machine learning

The ongoing digitization of the industrial-scale machines that power and enable human activity is itself a major global transformation. Joshua Bloom explains why the real revolution—in efficiencies and in improved and saved lives—will happen when machine learning automation and insights are properly coupled to the complex systems of industrial data. Leveraging a systems view of real-world use cases from aviation to transportation, Joshua contrasts the needs and approaches of consumer machine learning with industrial machine learning, focusing on three key areas: combining physics-based models with data-driven models, differential privacy and secure ML (including edge-to-cloud strategies), and interpretability of model predictions.

Joshua Bloom

GE Digital

Joshua Bloom is vice president of data and analytics at GE Digital, where he serves as the technology and research lead bringing machine learning applications to market within the GE ecosystem. Previously, Joshua was cofounder and CTO of Wise.io (acquired by GE Digital in 2016). Since 2005, he has also been an astronomy professor at the University of California, Berkeley, where he teaches astrophysics and Python for data science. Josh has been awarded the Moore Foundation Data-Driven Investigator Prize and the Pierce Prize from the American Astronomical Society; he is also a former Sloan fellow, a junior fellow of the Harvard Society, and a Hertz Foundation fellow. Joshua holds a PhD from Caltech and degrees from Harvard and Cambridge University.